This paper discusses a framework for algorithm-architecture synergy for (1) performance evaluation and (2) FPGA implementation complexity analysis of linear massive MIMO detection techniques. Three low complexity implementation techniques of the zero-forcing (ZF) based linear detection are evaluated, namely, Neumann series expansion (NSE), Gauss–Seidel (GS) and a proposed recursive Gram matrix inversion update (RGMIU) techniques. The performance analysis framework is based on software-defined radio platform. By extrapolating the real data measured average error vector magnitude versus a number of served single-antenna user terminals, GS and RGMIU are showing no performance degradation with respect to ZF with direct matrix inversion. It is shown that under high load regime NSE and GS require more processing iterations at the expense of increased processing latency. We, therefore, consider a unified approach for field-programmable gate array based implementation complexity analysis and discuss the required baseband processing resources for real-time transmission. Due to the wide differences of NSE, GS and RGMIU in terms of performance, processing complexity and latency, practical deployment and real-time implementation insights are derived.

本文讨论了一种用于算法-架构协同的框架，该框架用于（1）性能评估和（2）线性大规模MIMO检测技术的FPGA实现复杂性分析。对基于零强制（ZF）的线性检测的三种低复杂度实现技术进行了评估，分别是诺伊曼级数展开（NSE），高斯-赛德尔（GS）和提议的递归革兰氏矩阵求逆更新（RGMIU）技术。性能分析框架基于软件定义的无线电平台。通过外推实测数据相对于服务的单天线用户终端数量，测得的平均误差矢量幅度，相对于采用直接矩阵求逆的ZF而言，GS和RGMIU没有表现出性能下降。结果表明，在高负载状态下，NSE和GS需要进行更多的处理迭代，而代价是增加了处理延迟。因此，我们考虑基于现场可编程门阵列的实现复杂性分析的统一方法，并讨论实时传输所需的基带处理资源。由于NSE，GS和RGMIU在性能，处理复杂性和延迟方面存在很大差异，因此可以得出实际部署和实时实施的见解。